This Op takes a SparseTensor and is the sparse counterpart to
tf.reduce_sum(). In contrast to SparseReduceSum, this Op returns a
SparseTensor.
Reduces sp_input along the dimensions given in reduction_axes. Unless
keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
reduction_axes. If keep_dims is true, the reduced dimensions are retained
with length 1.
If reduction_axes has no entries, all dimensions are reduced, and a tensor
with a single element is returned. Additionally, the axes can be negative,
which are interpreted according to the indexing rules in Python.
Args
input_indices
A Tensor of type int64.
2-D. N x R matrix with the indices of non-empty values in a
SparseTensor, possibly not in canonical ordering.
input_values
A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, qint16, quint16, uint16, complex128, half, uint32, uint64.
1-D. N non-empty values corresponding to input_indices.
input_shape
A Tensor of type int64.
1-D. Shape of the input SparseTensor.
reduction_axes
A Tensor of type int32.
1-D. Length-K vector containing the reduction axes.
keep_dims
An optional bool. Defaults to False.
If true, retain reduced dimensions with length 1.
name
A name for the operation (optional).
Returns
A tuple of Tensor objects (output_indices, output_values, output_shape).